Quantum Programs for Simulating Open Systems

Abstract

The simulation of quantum mechanical systems was the initial, and still has been, the most prominent proposed application of quantum computing. Methods for simulating open quantum systems, where the system of interest interacts with an environment according to the Lindblad equation, are less explored compared to those for simulating closed systems. While a number of approaches already exist for simulating such open systems dynamics, the goal of the proposed research is to develop a novel paradigm for quantum simulation, called sample-based Lindbladian simulation for open dynamics. In particular, the PIs propose to develop further and explore this paradigm, including a novel algorithm called Wave Matrix Lindbladization (WML), in which samples of a program state are used in conjunction with a fixed interaction to simulate Lindbladian dynamics. This proposal outlines a number of exciting open questions surrounding sample-based Lindbladian simulation, including the following- Can the fixed interaction of WML be realized efficiently. Is the sample complexity of WML optimal. How does the algorithm perform when the program state is only an approximation of the desired Lindblad operator. Is the method proposed for WML unique, or could there be other approaches, with possibly better performance. How does the method extend to the case of infinite-dimensional bosonic systems. Answering the questions posed in this proposal will catapult this method to a competitive and broadly appealing approach for simulating open quantum systems. More generally, the ideas proposed here cut across multiple fields of science, including physics, computer science, mathematics, and engineering. The theme of this proposal is also consistent with the DoD mission to apply quantum information science to Air Force concerns and ensure they remain the most advanced and capable force in the World, given that AFRL is investigating quantum applications in algorithms, machine learning, neural network training, risk-decision analysis, quantum simulation of molecular and strongly correlated systems, photonic phenomena, and specific biological systems .

Document Details

Document Type
DoD Grant Award
Publication Date
Feb 05, 2025
Source ID
FA23862414069

Entities

People

  • Mark M. Wilde

Organizations

  • Air Force Office of Scientific Research
  • Cornell University
  • United States Air Force

Tags

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Distributed Systems and Data Platform Development
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Quantum Computing